Gümüş, Abdurrahman

Profile URL
https://hdl.handle.net/11147/15835
Name Variants
Gümüş, A.
Gumus, A
Gumus, Abdurrahman
Gumus, A.
Gümüş, A
Job Title
Email Address
abdurrahmangumus@iyte.edu.tr
Main Affiliation
03.05. Department of Electrical and Electronics Engineering
Status
Former Staff
Website
https://eee.iyte.edu.tr/wp-content/uploads/sites/88/2021/03/AbdurrahmanGumus_CV_20210301.pdf
ORCID ID
0000-0003-2993-5769
Scopus Author ID
35315599800
Turkish CoHE Profile ID
Google Scholar ID
Hc7A4o0AAAAJ
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
1
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
2
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QUALITY EDUCATION4
QUALITY EDUCATION
2
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
1
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
4
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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CLIMATE ACTION13
CLIMATE ACTION
1
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Documents

43

Citations

711

h-index

12

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This researcher does not have a WoS ID.
Scholarly Output

33

Articles

18

Views / Downloads

10654/19976

Supervised MSc Theses

7

Supervised PhD Theses

0

WoS Citation Count

116

Scopus Citation Count

136

Patents

0

Projects

3

WoS Citations per Publication

3.52

Scopus Citations per Publication

4.12

Open Access Source

13

Supervised Theses

7

JournalCount
-- 9th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2025 -- Gaziantep -- 2113422
Multimedia Tools and Applications2
Türk Doğa ve Fen Dergisi2
Applied Nano1
Applied Optics1
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Scholarly Output Search Results

Now showing 1 - 10 of 33
  • Master Thesis
    Deep Learning-Based Analysis of Electrochemical, Biomedical, and Optical Signals
    (01. Izmir Institute of Technology, 2024) Gümüş, Abdurrahman; Yeke, Muhammet Çağrı; Gümüş, Abdurrahman; Odacı, Dilek
    Bu tez, derin öğrenme (DÖ) tekniklerinin çeşitli alanlardaki uygulamalarını inceleyerek, karmaşık verilerin tespiti, sınıflandırılması ve analizi konularında önemli iyileştirmeler sağlamaktadır. Çalışma, DÖ modellerini farklı analitik yöntemlerle entegre ederek performansı artırmayı amaçlamaktadır. Elektrokimyasal analiz alanında, CD36'nın tespiti ve sınıflandırılması için bir immünobiyosensör kullanılarak DÖ tabanlı bir yaklaşım geliştirilmiştir. Geleneksel teknikler, özellikle düşük analit konsantrasyonlarında duyarlılık ve hızlı analizde yetersiz kalmaktadır. Tek boyutlu evrişimli sinir ağı (1B-ESA) ve hibrit 1B-ESA – uzun kısa süreli bellek (UKSB) ağları gibi DÖ modellerinin entegrasyonu, biyosensörün duyarlılığını ve özgüllüğünü önemli ölçüde artırmıştır. Biyomedikal uygulamalarda, yüzey elektromiyografi (yEMG) sinyalleri kullanılarak el hareketlerinin sınıflandırılması için Vision Transformer (ViT) teknikleri kullanılmıştır. sEMG verileri, ileri zaman-frekans analizi (TFA) yöntemleri ve çeşitli ViT modelleri ile analiz edilerek yüksek doğruluk elde edilmiştir. Optik algılama alanında, Faza Duyarlı - Zaman Bölgesinde Optik Geriyansımalı Ölçüm Tekniği (Faz-OTDR) verilerinin analizi için DÖ teknikleri kullanılmıştır. DÖ yöntemlerinin Faz-OTDR tabanlı akım algılama sistemlerinin verimliliğini artırdığı gösterilmiştir. 1B-ESA, 1B-ESA – UKSB ve 1B-ESA – Çift yönlü UKSB modelleri kullanılarak, akım değerlerinin doğru bir şekilde sınıflandırılması sağlanmıştır. Ayrıca, optik sinyalleri görüntüye çevirme metodu uygulanarak, aktarımlı öğrenme modelleri ile yüksek sınıflandırma doğruluğu elde edilmiş ve veri depolama daha verimli hale getirilmiştir. Bu tez, DÖ tekniklerinin çeşitli analitik yöntemlerle entegrasyonunun önemli ilerlemeler sağlama potansiyelini göstermektedir. Çalışmalar, DÖ'nün veri analizi performansını artırmadaki çok yönlülüğünü, daha doğru, hassas ve verimli çözümler sunarak ortaya koymaktadır. Geliştirilen metodolojiler, diğer biyomarkerlar, sinyal türleri ve analitik zorluklara genişletilebilir.
  • Conference Object
    Citation - Scopus: 3
    Ethereum Blockchain Smart Contract Vulnerability Detection Using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Demir,H.O.; Parlat,S.Z.; Gumus,A.
    Blockchain technology, employing advanced cryptography, stands as an optimal means to establish trust among unfamiliar online counterparts. It facilitates secure transactions and consensus among participants. Ethereum, a prominent blockchain network, extends this utility by introducing smart contracts. These are predefined programs containing data and methods for execution. Once deployed, these contracts remain unalterable due to blockchain's immutable nature. However, unlike conventional software that can be readily patched, they may harbor vulnerabilities. Smart contracts operate with the Ethereum cryptocurrency Ether, rendering fixes intricate and economically impactful. Static analyzers exist to spot vulnerabilities in smart contacts during development, but they are time-intensive. We propose a machine learning-based approach for detecting reentrancy vulnerabilities in smart contracts. Our system comprises three components: data preparation, Op2Vec, and an LSTM model. We collected 30,000 smart contracts, dividing them into two sets of 15,000 each for Op2Vec generation and LSTM training, respectively. We mapped opcode keywords to vector representations using a Skip-Gram algorithm, resulting in a 100-dimensional dictionary with 72 unique opcodes. Labeling was done using the Slither static analyzer, with 116 contracts identified as vulnerable and an additional 132 clean contracts for dataset balance. A Bidirectional LSTM (Bi-LSTM) model was devised by employing assembly data to detect flaws. The developed Bi-LSTM model demonstrated promise in reentrancy vulnerability detection, achieving a 96% accuracy rate in testing and reducing the analysis time to less than a fifth of that required by static analyzers. The codes and data are shared on GitHub as an open-source software package in a way that benefits everyone interested: https://github.com/miralabai/blockchain-vulnerability-detection. © 2023 IEEE.
  • Master Thesis
    Human-centric artificial intelligence systems for visual assistance and multimodal emotion analysis
    (01. Izmir Institute of Technology, 2024) Dede, İbrahim; Gümüş, Abdurrahman
    İnsan merkezli yapay zeka sistemleri, günlük yaşamı iyileştiren ve gerçek dünyadaki zorlukları ele alan teknolojiler yaratmak için çok önemlidir. Bu perspektifte, iki proje önerilmektedir. İlk proje olan Vis-Assist, görme engelli bireylere yardımcı olmak için tasarlanmış bir giyilebilir görsel yardımcı cihazdır. Nesneleri algılar ve sınıflandırır, mesafelerini ölçer ve harici sunuculara ihtiyaç duymadan entegre düşük maliyetli bir hesaplama birimi kullanarak titreşim motoru dizisi aracılığıyla gerçek zamanlı dokunsal geri bildirim sağlar. Bu cihaz, kullanıcıların 19 farklı nesne sınıfı arasında ayrım yapmasına ve güvenli bir şekilde gezinmesine olanak tanır. Geliştirilen giyilebilir cihazın performansı, dört katılımcıyla iki tür deney yoluyla değerlendirildi. Sonuçlar, kullanıcıların nesnelerin yerini belirleyebildiğini ve böylece engellerle çarpışmayı önleyebildiğini göstermektedir. Kullanıcılar ortalama olarak, 40 m²'lik boş bir alanda bir sandalye gibi önceden tanımlanmış bir nesneyi 94 saniyeden kısa bir sürede bulabilir ve nesneleri bulmak için engellerin etrafından dolaşabilir ve 121 saniyeden kısa bir sürede nesneleri bulabilir. İkinci proje, az sayıda atış öğrenmesi kullanarak çok modlu duygu sınıflandırmasına odaklanıyor. Yapay zekadaki geleneksel yöntemler, genellikle metin, görüntü, zaman serisi sinyali, ses spektrogramı gibi tek bir kaynak türünden gelen girdilere dayanır. Bu kaynaklar, modelin performansını iyileştirmek için çok modlu yaklaşımla birleştirilebilir. Bu araştırmada, OpenAI'nin CLIP çerçevesini kullanarak bir yapay zeka modeli geliştirildi ve Tip-Adapter algoritması üç tür girdiyi (metin, ses ve video) işleyecek şekilde uyarlandı. Modelin performansı, iki veri kümesi kullanılarak bir duygu sınıflandırma görevi üzerinde test edildi. Sonuçlar, çok modluluğun tek bir modalite kullanmaya kıyasla doğruluğu artırdığını göstererek, karmaşık, gerçek dünya ortamlarını anlayabilen ve bunlara yanıt verebilen insan merkezli AI sistemlerinin önemini vurguluyor.
  • Conference Object
    Citation - Scopus: 1
    Open-Source Visual Target-Tracking System Both on Simulation Environment and Real Unmanned Aerial Vehicles
    (Springer Science and Business Media Deutschland GmbH, 2024) Yılmaz,C.; Ozgun,A.; Erol,B.A.; Gumus,A.
    This work presents an investigation into the domain of dynamic target tracking through object detection, particularly emphasizing the context of open-source applications like PX4, ROS, and YOLO. Over the years, achieving real-time object tracking on UAVs in dynamic environments has been a formidable challenge, necessitating offline computations or substantial onboard processing resources. However, contemporary UAVs are now equipped with advanced edge embedded devices, sensors, and cameras, enabling the integration of deep learning-based vision applications. This advancement offers the prospect of directly deploying cutting-edge applications onto UAVs, thereby expanding their utility in areas such as surveillance, search and rescue, and videography. To fully harness the potential of these vision applications, a communication infrastructure interfacing with the UAV’s underneath closed controllers becomes imperative. We’ve developed an integrated visual target-tracking system that connects a flight controller unit with a graphical unit by leveraging ROS tools and open-source deep learning packages. The overall integrated system based on ROS, deep learning applications, and custom PID controllers is shared on GitHub as open-source software package in a way that benefits everyone interested: https://github.com/miralab-ai/vision-ROS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
  • Conference Object
    Development of Low-Cost Portable Blood Vessel Imaging System
    (IEEE, 2021) Altay, Ayse; Gumus, Abdurrahman
    As an alternative to high-cost near-infrared (NIR) vascular imaging devices in the market [1], a microcomputerbased, real-time, low-cost, non-contact and safe vascular imaging system has been developed. The higher absorption coefficient of blood from skin and fat, as well as the differences in oxy and deoxyhemoglobin spectra in blood, were helpful factors in the use of the NIR region during the acquisition of vessel images. A device, which uses NIR LED light operated at 850 nm, was designed using optical and electronic components. Image analysis were performed using OpenCV, which is an open-source software library, and data visualization libraries. Tests were carried out to optimize the best imaging conditions for the device. In this study, a portable device design with improved vessel image quality is presented which could potentially be used to assist the health professionals to investigate the abnormalities in the superficial vascular structures at different times during patients' treatments.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 5
    Diffusion-Based Data Augmentation Methodology for Improved Performance in Ocular Disease Diagnosis Using Retinography Images
    (Springer Heidelberg, 2024) Aktas, Burak; Ates, Doga Deniz; Duzyel, Okan; Gumus, Abdurrahman
    Deep learning models, integral components of contemporary technological landscapes, exhibit enhanced learning capabilities with larger datasets. Traditional data augmentation techniques, while effective in generating new data, have limitations, especially in fields like ocular disease diagnosis. In response, alternative augmentation approaches, including the utilization of generative AI, have emerged. In our study, we employed a diffusion-based model (Stable Diffusion) to synthesize data by faithfully recreating crucial vascular structures in the retina, vital for detecting eye diseases by using the Ocular Disease Intelligent Recognition dataset. Our goal was to augment retinography images for ocular disease diagnosis using diffusion-based models, optimizing the outputs of the fine-tuned Stable Diffusion model, and ensuring the generated data closely resembles real-world scenarios. This strategic approach resulted in improved performance in classification models and augmentation outperformed traditional methods, exhibiting high precision rates ranging from 85% to 76.2% and recall values of 86%, and 75% for 5 classes. Beyond performance enhancement, we demonstrated that the inclusion of synthetic data, coupled with data reduction using the t-SNE method, effectively addressed dataset imbalance. As a result of synthetic data addition, notable increases of 3.4% in the precision metric and 12.8% in the recall metric were observed in the 7-class case. Strategically synthesizing data addressed underrepresented classes, creating a balanced dataset for comprehensive model learning. Surpassing performance improvements, this approach underscores synthetic data's ability to overcome the limitations of traditional methods, particularly in sensitive medical domains like ocular disease diagnosis, ensuring accurate classification. The codes of the study will be shared on GitHub in a way that benefits everyone interested: https://github.com/miralab-ai/generative-data-augmentation.
  • Conference Object
    Semantic Guided Autoregressive Diffusion Based Data Augmentation Using Visual Instructions
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yavuzcan, Ege; Kus, Omer; Gumus, Abdurrahman
    Recent breakthroughs in generative image models, especially those based on diffusion techniques, have radically transformed the landscape of text-guided image synthesis by delivering exceptional fidelity and detailed semantic control. In this study, we present an iterative editing framework that harnesses the inherent strengths of these generative models to progressively refine images with precision. Our approach begins by generating diverse textual descriptions from an initial image, from which the most effective prompt is selected to drive further refinement through a fine-tuned Stable Diffusion process. This pipeline, as detailed in our flow diagram, orchestrates a series of controlled image modifications that preserve the original context while accommodating deliberate stylistic and semantic adjustments. By cycling the augmented output back into the system, our method achieves a harmonious balance between innovation and consistency, paving the way for highquality, context-aware visual transformations. This dynamic, auto-regressive strategy underscores the transformative potential of modern image generation models for applications that require detailed, controlled creative expression. The code is available on Github. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Iterative Semantic Refinement: A Vision Language Model-Driven Approach to Auto-Regressive Image Editing
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yavuzcan, Ege; Kus, Omer; Gumus, Abdurrahman
    Recent advancements in Visual Language Models (VLMs) have significantly improved text-to-image generation by enabling more nuanced and semantically rich textual prompts, highlighting the transformative impact of these models on image synthesis. In this work, we leverage these robust capabilities to develop an auto-regressive editing framework that systematically refines images through careful, step-by-step modifications. Our method concisely balances subtle adjustments with meaningful semantic shifts, ensuring that each editing stage preserves the core context while introducing precise variations. By integrating improvements from controllable image editing models, we enhance the precision and stability of our edits and demonstrate the effectiveness of our approach in maintaining visual coherence. This integration results in a powerful strategy for producing diverse, high-quality outputs that align with finely tuned semantic goals. Centered on the strength of VLMs, this framework opens up a new paradigm for image synthesis, offering a blend of creative flexibility and consistent contextual fidelity that holds promise for a variety of applications requiring intricate and controlled visual transformations. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Expandable Polymer Assisted Wearable Personalized Medicinal Platform
    (Wiley, 2020) Babatain, Wedyan; Wicaksono, Irmandy; Buttner, Ulrich; El-atab, Nazek; Rehman, Mutee Ur; Hussain, Muhammad Mustafa; Gümüş, Abdurrahman
    Conventional healthcare, thoughts of treatment, and practice of medicine largely rely on the traditional concept of one size fits all. Personalized medicine is an emerging therapeutic approach that aims to develop a therapeutic technique that provides tailor-made therapy based on everyone's individual needs by delivering the right drug at the right time with the right amount of dosage. Advancement in technologies such as wearable biosensors, point-of-care diagnostics, microfluidics, and artificial intelligence can enable the realization of effective personalized therapy. However, currently, there is a lack of a personalized minimally invasive wearable closed-loop drug delivery system that is continuous, automated, conformal to the skin, and cost-effective. Here, design, fabrication, optimization, and application of a personalized medicinal platform augmented with flexible biosensors, heaters, expandable actuator and processing units powered by a lightweight battery are shown. The platform provides precise drug delivery and preparation with spatiotemporal control over the administered dose as a response to real-time physiological changes of the individual. The system is conformal to the skin, and the drug is transdermally administered through an integrated microneedle. The developed platform is fabricated using rapid, cost-effective techniques that are independent of advanced microfabrication facilities to expand its applications to low-resource environments.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 10
    Surface Chemistry Dependent Toxicity of Inorganic Nanostructure Glycoconjugates on Bacterial Cells and Cancer Cell Lines
    (Elsevier, 2023) Sancak, Sedanur; Yazgan, İdris; Bayarslan, Aslı Uğurlu; Ayna, Adnan; Evecen, Senanur; Taşdelen, Zehra; Gümüş, Abdurrahman; Sönmez, Hamide Ayçin; Demir, Mehmet Ali; Demir, Sosin; Bakar, Fatma; Dilek Tepe, Hafize
    Surface functionalized nanostructures have outstanding potential in biological applications owing to their target-specific design. In this study, we utilized laboratory synthesized carbohydrate-derivatives (i.e., galactose, mannose, lactose, and cellobiose derivatives) for aqueous one-pot synthesis of gold (Au) and silver (Ag) nanostructure glycoconjugates (NSs), and iron metal-organic framework glycoconjugates (FeMOFs). This work aims to test whether differences in the surface chemistry of the inorganic nanostructures play roles in revealing their toxicities towards bacterial cells and cancerous cell lines. As of the first step, biological activity of AuNSs, AgNSs, and FeMOFs were tested against a variety of gram (−) and gram (+) bacterial strains, where AgNSs possessed moderate to high antibacterial activities against all the tested bacterial strains, while AuNSs and FeMOFs showed their bacterial toxicity mostly depending on the strain. Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) determination studies were performed for the nanostructure glycoconjugates, for which μg/mL MBC values were obtained such as (Cellobiose p-aminobenzoic acid_AgNS) CBpAB_AgNS gave 50 μg/mL MBC value for P.aeruginosa and S.kentucy. The activity of selected sugar ligands and corresponding glycoconjugates were further tested on MDA-MB-231 breast cancer and A549 lung cancer cell lines, where selective anticancer activity was observed depending on the surface chemistry as well. Besides, D-penicillamine was introduced to galectin specific sugar ligand coated AuNS glycoconjugates, which showed very strong anticancer activities even at low doses. Overall, the importance of this work is that the surface chemistry of the inorganic nanostructures can be critical to reveal their toxicity towards bacterial cells and cancerous cell lines.